

'''simple docstring'''
import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_lowerCamelCase      					=     logging.get_logger(__name__)

_lowerCamelCase      					=     {
    """microsoft/conditional-detr-resnet-50""": (
        """https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"""
    ),
}
class 					_snake_case		(lowercase__):
			__A  :	str        ="conditional_detr"
			__A  :	Any        =["past_key_values"]
			__A  :	Tuple        ={
			    "hidden_size": "d_model",
			    "num_attention_heads": "encoder_attention_heads",
			}






			def __init__(  self			,_snake_case=True			,_snake_case=None			,_snake_case=3			,_snake_case=3_00			,_snake_case=6			,_snake_case=20_48			,_snake_case=8			,_snake_case=6			,_snake_case=20_48			,_snake_case=8			,_snake_case=0.0			,_snake_case=0.0			,_snake_case=True			,_snake_case="relu"			,_snake_case=2_56			,_snake_case=0.1			,_snake_case=0.0			,_snake_case=0.0			,_snake_case=0.02			,_snake_case=1.0			,_snake_case=False			,_snake_case="sine"			,_snake_case="resnet50"			,_snake_case=True			,_snake_case=False			,_snake_case=2			,_snake_case=5			,_snake_case=2			,_snake_case=1			,_snake_case=1			,_snake_case=2			,_snake_case=5			,_snake_case=2			,_snake_case=0.25			,**_snake_case			,):


				if backbone_config is not None and use_timm_backbone:
					raise ValueError("You can\'t specify both `backbone_config` and `use_timm_backbone`."     )

				if not use_timm_backbone:
					if backbone_config is None:
						logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."     )
						UpperCAmelCase_     :      int						=  CONFIG_MAPPING["resnet"](out_features=["stage4"]     )
					elif isinstance(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_     ):
						UpperCAmelCase_     :      Optional[Any]						=  backbone_config.get("model_type"     )
						UpperCAmelCase_     :      str						=  CONFIG_MAPPING[backbone_model_type]
						UpperCAmelCase_     :      List[Any]						=  config_class.from_dict(SCREAMING_SNAKE_CASE_     )

				UpperCAmelCase_     :      Union[str, Any]						=  use_timm_backbone
				UpperCAmelCase_     :      Any						=  backbone_config
				UpperCAmelCase_     :      List[str]						=  num_channels
				UpperCAmelCase_     :      List[str]						=  num_queries
				UpperCAmelCase_     :      Union[str, Any]						=  d_model
				UpperCAmelCase_     :      List[Any]						=  encoder_ffn_dim
				UpperCAmelCase_     :      Any						=  encoder_layers
				UpperCAmelCase_     :      int						=  encoder_attention_heads
				UpperCAmelCase_     :      Union[str, Any]						=  decoder_ffn_dim
				UpperCAmelCase_     :      Optional[Any]						=  decoder_layers
				UpperCAmelCase_     :      Optional[int]						=  decoder_attention_heads
				UpperCAmelCase_     :      Tuple						=  dropout
				UpperCAmelCase_     :      Any						=  attention_dropout
				UpperCAmelCase_     :      Union[str, Any]						=  activation_dropout
				UpperCAmelCase_     :      List[str]						=  activation_function
				UpperCAmelCase_     :      Optional[Any]						=  init_std
				UpperCAmelCase_     :      List[str]						=  init_xavier_std
				UpperCAmelCase_     :      Optional[Any]						=  encoder_layerdrop
				UpperCAmelCase_     :      int						=  decoder_layerdrop
				UpperCAmelCase_     :      Dict						=  encoder_layers
				UpperCAmelCase_     :      int						=  auxiliary_loss
				UpperCAmelCase_     :      str						=  position_embedding_type
				UpperCAmelCase_     :      int						=  backbone
				UpperCAmelCase_     :      Optional[int]						=  use_pretrained_backbone
				UpperCAmelCase_     :      Any						=  dilation
				# Hungarian matcher
				UpperCAmelCase_     :      Tuple						=  class_cost
				UpperCAmelCase_     :      Tuple						=  bbox_cost
				UpperCAmelCase_     :      Dict						=  giou_cost
				# Loss coefficients
				UpperCAmelCase_     :      str						=  mask_loss_coefficient
				UpperCAmelCase_     :      Optional[int]						=  dice_loss_coefficient
				UpperCAmelCase_     :      Dict						=  cls_loss_coefficient
				UpperCAmelCase_     :      Optional[int]						=  bbox_loss_coefficient
				UpperCAmelCase_     :      str						=  giou_loss_coefficient
				UpperCAmelCase_     :      str						=  focal_alpha
				super().__init__(is_encoder_decoder=SCREAMING_SNAKE_CASE_			,**SCREAMING_SNAKE_CASE_     )






			@property
			def 		UpperCamelCase__							(  self     ):
				return self.encoder_attention_heads






			@property
			def 		UpperCamelCase__							(  self     ):
				return self.d_model






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Union[str, Any]						=  copy.deepcopy(self.__dict__     )
				if self.backbone_config is not None:
					UpperCAmelCase_     :      List[Any]						=  self.backbone_config.to_dict()
				UpperCAmelCase_     :      str						=  self.__class__.model_type
				return output




class 					_snake_case		(lowercase__):
			__A  :	Union[str, Any]        =version.parse("1.11")






			@property
			def 		UpperCamelCase__							(  self     ):
				return OrderedDict(
				    [
				        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
				        ("pixel_mask", {0: "batch"}),
				    ]     )






			@property
			def 		UpperCamelCase__							(  self     ):
				return 1E-5






			@property
			def 		UpperCamelCase__							(  self     ):
				return 12






"""simple docstring"""






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class 			lowercase					( lowercase__		):
			lowercase        =     ['''image_processor''', '''tokenizer''']
			lowercase        =     '''CLIPImageProcessor'''
			lowercase        =     ('''XLMRobertaTokenizer''', '''XLMRobertaTokenizerFast''')






			def __init__(self  :	Union[str, Any]      ,SCREAMING_SNAKE_CASE_  :	int=None      ,SCREAMING_SNAKE_CASE_  :	str=None      ,**SCREAMING_SNAKE_CASE_  :	Union[str, Any]			)       ->	int:





				"""simple docstring"""


				lowerCAmelCase												=  None
				if "feature_extractor" in kwargs:
					warnings.warn(
					    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
					    ''' instead.'''      ,SCREAMING_SNAKE_CASE_      ,)
					lowerCAmelCase												=  kwargs.pop('''feature_extractor'''			)

				lowerCAmelCase												=  image_processor if image_processor is not None else feature_extractor
				if image_processor is None:
					raise ValueError('''You need to specify an `image_processor`.'''			)
				if tokenizer is None:
					raise ValueError('''You need to specify a `tokenizer`.'''			)

				super().__init__(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)






			def __call__(self  :	Union[str, Any]      ,SCREAMING_SNAKE_CASE_  :	List[str]=None      ,SCREAMING_SNAKE_CASE_  :	Optional[int]=None      ,SCREAMING_SNAKE_CASE_  :	Union[str, Any]=None      ,**SCREAMING_SNAKE_CASE_  :	Any			)       ->	List[Any]:





				"""simple docstring"""


				if text is None and images is None:
					raise ValueError('''You have to specify either text or images. Both cannot be none.'''			)

				if text is not None:
					lowerCAmelCase												=  self.tokenizer(SCREAMING_SNAKE_CASE_      ,return_tensors=SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)

				if images is not None:
					lowerCAmelCase												=  self.image_processor(SCREAMING_SNAKE_CASE_      ,return_tensors=SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)

				if text is not None and images is not None:
					lowerCAmelCase												=  image_features.pixel_values
					return encoding
				elif text is not None:
					return encoding
				else:
					return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE_			)      ,tensor_type=SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	List[str]      ,*SCREAMING_SNAKE_CASE_  :	Union[str, Any]      ,**SCREAMING_SNAKE_CASE_  :	Tuple			)       ->	Optional[Any]:





				"""simple docstring"""


				return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	Union[str, Any]      ,*SCREAMING_SNAKE_CASE_  :	int      ,**SCREAMING_SNAKE_CASE_  :	Union[str, Any]			)       ->	Any:





				"""simple docstring"""


				return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)






			@property
			def 					UpperCAmelCase							(self  :	List[Any]			)       ->	Dict:





				"""simple docstring"""


				lowerCAmelCase												=  self.tokenizer.model_input_names
				lowerCAmelCase												=  self.image_processor.model_input_names
				return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)


